scholarly journals An explicit-solvent conformation search method using open software

PeerJ ◽  
2016 ◽  
Vol 4 ◽  
pp. e2088 ◽  
Author(s):  
Kari Gaalswyk ◽  
Christopher N. Rowley
Keyword(s):  
Gas Phase ◽  
Work Flow ◽  
Conformational Search ◽  
Implicit Solvent ◽  
Drug Molecules ◽  
Explicit Solvent ◽  
Solvent Model ◽  
Solvated Molecules ◽  
Simulated Trajectory ◽  
The Stability

Computer modeling is a popular tool to identify the most-probable conformers of a molecule. Although the solvent can have a large effect on the stability of a conformation, many popular conformational search methods are only capable of describing molecules in the gas phase or with an implicit solvent model. We have developed a work-flow for performing a conformation search on explicitly-solvated molecules using open source software. This method uses replica exchange molecular dynamics (REMD) to sample the conformational states of the molecule efficiently. Cluster analysis is used to identify the most probable conformations from the simulated trajectory. This work-flow was tested on drug molecules α-amanitin and cabergoline to illustrate its capabilities and effectiveness. The preferred conformations of these molecules in gas phase, implicit solvent, and explicit solvent are significantly different.

scholarly journals An explicit-solvent conformation search method using open software

2016 ◽  
Author(s):  
Kari Gaalswyk ◽  
Christopher N Rowley
Keyword(s):  
Gas Phase ◽  
Work Flow ◽  
Conformational Search ◽  
Implicit Solvent ◽  
Drug Molecules ◽  
Explicit Solvent ◽  
Solvent Model ◽  
Solvated Molecules ◽  
Simulated Trajectory ◽  
The Stability

Computer modeling is a popular tool to identify the most-probable conformers of a molecule. Although the solvent can have a large effect on the stability of a conformation, many popular conformational search methods are only capable of describing molecules in the gas phase or with an implicit solvent model. We have developed a work-flow for performing a conformation search on explicitly-solvated molecules using open source software. This method uses replica exchange molecular dynamics to sample the conformational states of the molecule efficiently. Cluster analysis is used to identify the most probable conformations from the simulated trajectory. This work-flow was tested on drug molecules a-amanitin and cabergoline to illustrate its capabilities and effectiveness. The preferred conformations of these molecules in gas phase, implicit solvent, and explicit solvent are significantly different.

scholarly journals An explicit-solvent conformation search method using open software

2016 ◽  
Author(s):  
Kari Gaalswyk ◽  
Christopher N Rowley
Keyword(s):  
Gas Phase ◽  
Work Flow ◽  
Conformational Search ◽  
Implicit Solvent ◽  
Drug Molecules ◽  
Explicit Solvent ◽  
Solvent Model ◽  
Solvated Molecules ◽  
Simulated Trajectory ◽  
The Stability

Computer modeling is a popular tool to identify the most-probable conformers of a molecule. Although the solvent can have a large effect on the stability of a conformation, many popular conformational search methods are only capable of describing molecules in the gas phase or with an implicit solvent model. We have developed a work-flow for performing a conformation search on explicitly-solvated molecules using open source software. This method uses replica exchange molecular dynamics to sample the conformational states of the molecule efficiently. Cluster analysis is used to identify the most probable conformations from the simulated trajectory. This work-flow was tested on drug molecules a-amanitin and cabergoline to illustrate its capabilities and effectiveness. The preferred conformations of these molecules in gas phase, implicit solvent, and explicit solvent are significantly different.

scholarly journals Bridging the experiment-calculation divide: machine learning corrections to redox potential calculations in implicit and explicit solvent models

2021 ◽  
Author(s):  
Eugen Hruska ◽  
Ariel Gale ◽  
Fang Liu
Keyword(s):  
Machine Learning ◽  
Redox Potential ◽  
Density Functional ◽  
Implicit Solvent ◽  
Systematic Bias ◽  
Redox Potentials ◽  
Solvent Models ◽  
Explicit Solvent ◽  
Solvent Model ◽  
Implicit And Explicit

Prediction of redox potentials is essential for catalysis and energy storage. Although density functional theory (DFT) calculations have enabled rapid redox potential predictions for numerous compounds, prominent errors persist compared to experimental measurements. In this work, we develop machine learning (ML) models to reduce the errors of redox potential calculations in both implicit and explicit solvent models. Training and testing of the ML correction models are based on the diverse ROP313 dataset with experimental redox potentials measured for organic and organometallic compounds in a variety of solvents. For the implicit solvent approach, our ML models can reduce both the systematic bias and the number of outliers. ML corrected redox potentials also demonstrate less sensitivity to DFT functional choice. For the explicit solvent approach, we significantly reduce the computational costs by embedding the microsolvated cluster in implicit bulk solvent, obtaining converged redox potential results with a smaller solvation shell. This combined implicit-explicit solvent model, together with GPU-accelerated quantum chemistry methods, enabled rapid generation of a large dataset of explicit-solvent-calculated redox potentials for 165 organic compounds, allowing detailed investigation of the error sources in explicit solvent redox potential calculations.

scholarly journals Bridging the experiment-calculation divide: machine learning corrections to redox potential calculations in implicit and explicit solvent models

2021 ◽  
Author(s):  
Eugen Hruska ◽  
Ariel Gale ◽  
Fang Liu
Keyword(s):  
Machine Learning ◽  
Redox Potential ◽  
Density Functional ◽  
Implicit Solvent ◽  
Systematic Bias ◽  
Redox Potentials ◽  
Solvent Models ◽  
Explicit Solvent ◽  
Solvent Model ◽  
Implicit And Explicit

Prediction of redox potentials is essential for catalysis and energy storage. Although density functional theory (DFT) calculations have enabled rapid redox potential predictions for numerous compounds, prominent errors persist compared to experimental measurements. In this work, we develop machine learning (ML) models to reduce the errors of redox potential calculations in both implicit and explicit solvent models. Training and testing of the ML correction models are based on the diverse ROP313 dataset with experimental redox potentials measured for organic and organometallic compounds in a variety of solvents. For the implicit solvent approach, our ML models can reduce both the systematic bias and the number of outliers. ML corrected redox potentials also demonstrate less sensitivity to DFT functional choice. For the explicit solvent approach, we significantly reduce the computational costs by embedding the microsolvated cluster in implicit bulk solvent, obtaining converged redox potential results with a smaller solvation shell. This combined implicit-explicit solvent model, together with GPU-accelerated quantum chemistry methods, enabled rapid generation of a large dataset of explicit-solvent-calculated redox potentials for 165 organic compounds, allowing detailed investigation of the error sources in explicit solvent redox potential calculations.

scholarly journals Microsolvation of Histidine—A Theoretical Study of Intermolecular Interactions Based on AIM and SAPT Approaches

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1153
Author(s):  
Beata Kizior ◽  
Jarosław J. Panek ◽  
Aneta Jezierska
Keyword(s):  
Gas Phase ◽  
Density Functional ◽  
Heterocyclic Ring ◽  
Polarizable Continuum Model ◽  
Water Molecules ◽  
Implicit Solvent ◽  
Actual Interaction ◽  
The Stability ◽  
Explicit Water ◽  
The Impact

Histidine is unique among amino acids because of its rich tautomeric properties. It participates in essential enzymatic centers, such as catalytic triads. The main aim of the study is the modeling of the change of molecular properties between the gas phase and solution using microsolvation models. We investigate histidine in its three protonation states, microsolvated with 1:6 water molecules. These clusters are studied computationally, in the gas phase and with water as a solvent (Polarizable Continuum Model, PCM) within the Density Functional Theory (DFT) framework. The structural analysis reveals the presence of intra- and intermolecular hydrogen bonds. The Atoms-in-Molecules (AIM) theory is employed to determine the impact of solvation on the charge flow within the histidine, with emphasis on the similarity of the two imidazole nitrogen atoms—topologically not equivalent, they are revealed as electronically similar due to the heterocyclic ring aromaticity. Finally, the Symmetry-Adapted Perturbation Theory (SAPT) is used to examine the stability of the microsolvation clusters. While electrostatic and exchange terms dominate in magnitude over polarization and dispersion, the sum of electrostatic and exchange term is close to zero. This makes polarization the factor governing the actual interaction energy. The most important finding of this study is that even with microsolvation, the polarization induced by the presence of implicit solvent is still significant. Therefore, we recommend combined approaches, mixing explicit water molecules with implicit models.

scholarly journals Urea as a Cocrystal Former—Study of 3 Urea Based Pharmaceutical Cocrystals

Pharmaceutics ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 671
Author(s):  
Fucheng Leng ◽  
Koen Robeyns ◽  
Tom Leyssens
Keyword(s):  
Ellagic Acid ◽  
Physical Property ◽  
High Humidity ◽  
Pharmaceutical Cocrystals ◽  
Drug Molecules ◽  
Temperature Environment ◽  
Pharmaceutical Cocrystal ◽  
The Stability ◽  
As Solubility ◽  
High Temperature Environment

Cocrystallization is commonly used for its ability to improve the physical properties of APIs, such as solubility, bioavailability, compressibility, etc. The pharmaceutical industry is particularly interested in those cocrystals comprising a GRAS former in connection with the target API. In this work, we focus on the potential of urea as a cocrystal former, identifying three novel pharmaceutical cocrystal systems with catechin, 3-hydroxyl-2-naphthoic and ellagic acid. Interestingly, the stability of catechin under high humidity or high temperature environment is improved upon cocrystallization with urea. Moreover, the solubility of ellagic acid is improved about 17 times. This work displays the latent possibility of urea in improving the physical property of drug molecules using a cocrystallization approach.

The conformational search, the stability, fragment interaction and resistance to acidic attack of epoxyl-polyurethanes in different solvent media

2019 ◽  
Vol 31 (3) ◽  
pp. 861-875 ◽  
Author(s):  
Adebayo A. Adeniyi ◽  
Cecilia O. Akintayo ◽  
Emmanuel T. Akintayo ◽  
Jeanet Conradie
Keyword(s):  
Conformational Search ◽  
The Stability ◽  
Fragment Interaction
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